!pip install fredapi

Requirement already satisfied: fredapi in c:\users\joseph ruyle\anaconda3\lib\site-packages (0.5.2)
Requirement already satisfied: pandas in c:\users\joseph ruyle\anaconda3\lib\site-packages (from fredapi) (2.2.2)
Requirement already satisfied: numpy>=1.26.0 in c:\users\joseph ruyle\anaconda3\lib\site-packages (from pandas->fredapi) (1.26.4)
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import time

plt.style.use('fivethirtyeight')
pd.set_option('display.max_columns', 500)
color_pal = plt.rcParams["axes.prop_cycle"].by_key()["color"]

from fredapi import Fred

fred_key = '39545b97105ca91a853a006638421a1d'

fred = Fred(api_key=fred_key)

sp_search = fred.search('S&P', order_by='popularity')

sp_search.head()

sp500 = fred.get_series(series_id='SP500')
sp500.plot(figsize=(10, 5), title='S&P 500', lw=2)
plt.show()

unemp_df = fred.search('unemployment rate state', filter=('frequency','Monthly'))
unemp_df = unemp_df.query('seasonal_adjustment == "Seasonally Adjusted" and units == "Percent"')
unemp_df = unemp_df.loc[unemp_df['title'].str.contains('Unemployment Rate')]

all_results = []

for myid in unemp_df.index:
    results = fred.get_series(myid)
    results = results.to_frame(name=myid)
    all_results.append(results)
    time.sleep(0.1) # Don't request to fast and get blocked
uemp_results = pd.concat(all_results, axis=1)

cols_to_drop = []
for i in uemp_results:
    if len(i) > 4:
        cols_to_drop.append(i)
uemp_results = uemp_results.drop(columns = cols_to_drop, axis=1)

uemp_states = uemp_results.copy()  #.drop('UNRATE', axis=1)
uemp_states = uemp_states.dropna()
id_to_state = unemp_df['title'].str.replace('Unemployment Rate in ','').to_dict()
uemp_states.columns = [id_to_state[c] for c in uemp_states.columns]

px.line(uemp_states)

ax = uemp_states.loc[uemp_states.index == '2020-05-01'].T \
    .sort_values('2020-05-01') \
    .plot(kind='barh', figsize=(8, 12), width=0.7, edgecolor='black',
          title='Unemployment Rate by State, May 2020')
ax.legend().remove()
ax.set_xlabel('% Unemployed')
plt.show()

part_df = fred.search('participation rate state', filter=('frequency','Monthly'))
part_df = part_df.query('seasonal_adjustment == "Seasonally Adjusted" and units == "Percent"')

part_id_to_state = part_df['title'].str.replace('Labor Force Participation Rate for ','').to_dict()

all_results = []

for myid in part_df.index:
    results = fred.get_series(myid)
    results = results.to_frame(name=myid)
    all_results.append(results)
    time.sleep(0.1) # Don't request to fast and get blocked
part_states = pd.concat(all_results, axis=1)
part_states.columns = [part_id_to_state[c] for c in part_states.columns]

uemp_states = uemp_states.rename(columns={'the District of Columbia':'District Of Columbia'})

fig, axs = plt.subplots(10, 5, figsize=(30, 30), sharex=True)
axs = axs.flatten()

i = 0
for state in uemp_states.columns:
    if state in ["District Of Columbia","Puerto Rico"]:
        continue
    ax2 = axs[i].twinx()
    uemp_states.query('index >= 2020 and index < 2022')[state] \
        .plot(ax=axs[i], label='Unemployment')
    part_states.query('index >= 2020 and index < 2022')[state] \
        .plot(ax=ax2, label='Participation', color=color_pal[1])
    ax2.grid(False)
    axs[i].set_title(state)
    i += 1
plt.tight_layout()
plt.show()

state = 'California'
fig, ax = plt.subplots(figsize=(10, 5), sharex=True)
ax2 = ax.twinx()
uemp_states2 = uemp_states.asfreq('MS')
l1 = uemp_states2.query('index >= 2020 and index < 2022')[state] \
    .plot(ax=ax, label='Unemployment')
l2 = part_states.dropna().query('index >= 2020 and index < 2022')[state] \
    .plot(ax=ax2, label='Participation', color=color_pal[1])
ax2.grid(False)
ax.set_title(state)
fig.legend(labels=['Unemployment','Participation'])
plt.show()

	id	realtime_start	realtime_end	title	observation_start	observation_end	frequency	frequency_short	units	units_short	seasonal_adjustment	seasonal_adjustment_short	last_updated	popularity	notes
series id
BAMLH0A0HYM2	BAMLH0A0HYM2	2024-07-05	2024-07-05	ICE BofA US High Yield Index Option-Adjusted S...	1996-12-31	2024-07-04	Daily, Close	D	Percent	%	Not Seasonally Adjusted	NSA	2024-07-05 09:22:14-05:00	92	The ICE BofA Option-Adjusted Spreads (OASs) ar...
CSUSHPINSA	CSUSHPINSA	2024-07-05	2024-07-05	S&P CoreLogic Case-Shiller U.S. National Home ...	1987-01-01	2024-04-01	Monthly	M	Index Jan 2000=100	Index Jan 2000=100	Not Seasonally Adjusted	NSA	2024-06-25 08:12:01-05:00	88	For more information regarding the index, plea...
SP500	SP500	2024-07-05	2024-07-05	S&P 500	2014-07-07	2024-07-03	Daily, Close	D	Index	Index	Not Seasonally Adjusted	NSA	2024-07-03 19:16:40-05:00	83	The observations for the S&P 500 represent the...
MEHOINUSA672N	MEHOINUSA672N	2024-07-05	2024-07-05	Real Median Household Income in the United States	1984-01-01	2022-01-01	Annual	A	2022 CPI-U-RS Adjusted Dollars	2022 CPI-U-RS Adjusted $	Not Seasonally Adjusted	NSA	2023-09-12 13:48:02-05:00	83	Household data are collected as of March. As ...
BAMLH0A0HYM2EY	BAMLH0A0HYM2EY	2024-07-05	2024-07-05	ICE BofA US High Yield Index Effective Yield	1996-12-31	2024-07-04	Daily, Close	D	Percent	%	Not Seasonally Adjusted	NSA	2024-07-05 09:22:16-05:00	82	This data represents the effective yield of th...